Spatially Discriminated Object Classification With Physics Models
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Solution Overview
Problem
Existing automatic target recognition (ATR) systems face challenges due to reliance on large amounts of labeled training data, field performance fragility, rigidity in adapting to new classes, lack of transparency, and model mismatch errors, particularly in inverse synthetic aperture radar (ISAR) image classification.
Innovation Solution
A hybrid approach combining model-based and data-based methods, utilizing a ship model defined by high-level features and a data model for feature manifestation, with a convolutional neural network (CNN) to learn data-to-feature mapping, and a probabilistic framework for decision-making, enabling online learning and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for object classification, then classification performance is improved, but the system requires enormous amounts of labeled training data
Solution Approach 1:
The patent introduces a physics-based forward model as an intermediary that bridges the gap between raw sensor data and object classification. This model simulates how objects should appear in radar images based on physical principles, allowing the system to generate synthetic training data and reduce dependency on real labeled data while maintaining classification accuracy.
Solution Approach 2:
The system creates synthetic copies of real radar images by applying the forward model to generate simulated radar returns. These synthetic images replicate the appearance of real targets without requiring actual labeled data, enabling the deep learning model to train on abundant synthetic examples rather than scarce real labeled data.
2Measurement precision
If deep learning methods are used for object classification, then classification accuracy is improved, but field performance becomes fragile when training data does not represent real-world variations
Solution Approach 1:
The forward model allows systematic variation of physical parameters such as target material properties, geometric dimensions, and radar scattering characteristics. By training the deep learning model on synthetic data generated from these parameter variations, the system learns robust features that generalize to real-world conditions without requiring explicit representation of all possible variations in the training data.
3Adaptability or versatility
If model-based methods are used for object classification, then adaptability to new classes is improved, but model complexity and computational burden increase
Solution Approach 1:
The patent segments the classification system into two independent components: a physics-based forward model that handles object generation and a data-driven deep learning model that handles classification. This segmentation allows the forward model to be simplified for computational efficiency while the neural network handles the complex pattern recognition, achieving both adaptability and computational feasibility.
4Measurement precision
If deep learning methods are used for object classification, then classification capability is improved, but transparency and explainability of decision-making process deteriorates
Solution Approach 1:
The forward model serves as an interpretable intermediary that provides explicit physical reasoning about how objects generate radar signatures. By comparing actual sensor data with forward model predictions, the system can explain classification decisions in terms of physical properties and geometric relationships, making the black-box neural network more transparent through its physics-based rationale.
Data Source
AI summary
Devices, systems, and methods for machine learning (ML) automatic target recognition (ATR) decision explanation are provided. A method can include receiving an object specification matrix from an object model database that indicates, for each of a plurality of physical portions of an object, whether each of a plurality of features are present or absent in a physical portion of the physical portions of the object and a proportional physical displacement between the features in the object, receiving feature data indicating for an image of a portion of the object, a likelihood whether each of features are present in the image, determining based on the object specification matrix and the feature data, a probability and corresponding uncertainty that the image corresponds to the object, and providing the probability and corresponding uncertainty of the object to help classify the object.